Chunking and Embedding Strategies

How you split and embed a corpus sets the ceiling on RAG quality — the levers, the trade-offs, and how to choose empirically

Deep dive on the two decisions that most determine RAG retrieval quality: chunking strategy (fixed-size, structure-aware, semantic, parent-child), overlap, chunk size vs recall, embedding model selection, and embedding drift when you swap models — with worked recall@k examples.

Practice questions (5)

• Choosing a Chunking Strategy for a Legal Contract Corpus

  Intermediate · Free
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• Diagnosing a Recall Drop After a Chunk-Size Change

  Intermediate
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• Planning an Embedding Model Migration

  Intermediate
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• Designing Chunk Metadata for a Multi-Tenant, Multi-Product Knowledge Base

  Intermediate
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• Designing an Empirical Comparison of Two Chunking Configs

  Intermediate
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Quizzes (1)